Beyond Content: A Trauma-Informed Framework for Academic Writing Evaluation

Abstract

The paper proposes a novel framework for assessing student academic writing integrating an understanding of trauma into the evaluation process. This framework emphasises the importance of recognising linguistic markers in student writing that may indicate underlying psychological distress, such as post-traumatic stress disorder (PTSD). Traditional higher education academic assessments often overlook these markers, focusing on content quality and adherence to formal writing standards. This oversight could lead to missed opportunities for early intervention, particularly in educational settings where students do not openly disclose their mental health challenges. Building on trauma-informed pedagogy, this study explores how Natural Language Processing (NLP) models can be leveraged to identify linguistic markers associated with poor mental health, such as PTSD, anxiety, and depression, within students' academic writing. By analysing writing patterns like non-linear narrative structures, obsessive thoughts, and disjointed syntax, we argue that NLP can offer an essential tool for early detection of trauma-related challenges. Such markers are often overlooked in traditional grading systems, which prioritize form and rhetoric. A case study using student writing samples demonstrates how changes in rhetorical fluency and writing quality can correlate with a documented decline in mental health. The results of NLP analysis reveal a progressive decline in coherence, lexical diversity, and thematic focus, which align with known linguistic markers of trauma. These findings underscore the potential of NLP to serve as an early-warning system, alerting educators to the need for intervention and support. Despite the promise of these methods, current NLP models face limitations in linguistic diversity, reproducibility, and population bias. Therefore, we advocate for the development of more inclusive models built on ethical frameworks that consider the socio-rhetorical contexts of student writing. Additionally, large and secure datasets are required to ensure representativeness, with attention to student privacy concerns. Ultimately, this paper calls for higher education institutions to adopt trauma-sensitive evaluation frameworks that integrate academic and emotional well-being, ensuring more equitable and compassionate assessments.